Elsevier

Psychiatry Research

Volume 296, February 2021, 113677
Psychiatry Research

Review article
Characteristics of sleep architecture in autism spectrum disorders: A meta-analysis based on polysomnographic research

https://doi.org/10.1016/j.psychres.2020.113677Get rights and content

Highlights

  • Autism spectrum disorders have attracted extensive attention in recent years, and their sleep problems are worthy further exploreding.

  • Polysomnography is the gold standard for evaluating sleep disorders in autism spectrum disorders.

  • Sleep problems are more prominent in autism spectrum disorders than in typically developed individuals while studies illustrating specific difference of sleep architecture between the twos systematically in the whole sleep cycle have not been found yet.

Abstract

Eleven parameters recorded by polysomnography were used to evaluate the differences in sleep structure between individuals with autism spectrum disorders (ASDs) and typically developed individuals (TDs). Four databases (PubMed, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI)) were searched for potentially relevant literature published before July 14, 2019. Data extraction was performed by two independent assessors. The Cohen's d effect sizes and their 95% confidence intervals (CIs) were calculated to assess the effectiveness with the random-effects model. The heterogeneity was estimated by Cochran's Q test. The research yielded 14 case-control studies, 11 of which were included in this meta-analysis. Synthesis of the differences in 11 sleep parameters between individuals with ASDs and TDs demonstrated the pooled effect size of Cohen'd was -0.52 (95% CI: (-0.97, -0.08)) for total sleep time (TST), -0.69 (95% CI: (-1.27, -0.11)) for sleep efficiency (SE%) and 0.93 (95% CI: (0.37, 1.48)) for stage 1 sleep (S1%), respectively. Our findings suggested that compared with TDs, individuals with ASDs tend to have a decreased TST and SE% and an increased S1%. Differences of characteristics of sleep architecture in other sleep parameters between individuals with ASDs and TDs were not found in this study.

Introduction

An increasing number of studies have focused on autism spectrum disorders (ASDs) in recent years. ASDs are essentially a class of neurodevelopmental disorders, that lead to a lack of social interaction, repetitive behaviors and language communication disorders (Solberg et al., 2019). According to the fifth edition of the Diagnostic And Statistical Manual of Mental Disorders published in 2013, ASDs comprise several subtypes, including autism, Asperger's syndrome and other unclassified pervasive developmental disorders (Fredrik, 2013). Moreover, the comorbidities of ASDs are numerous, including attention-deficit/hyperactivity disorder, epilepsy, and mental deficiency. Sleep disorders are also common comorbidities that have been widely reported, with mixed results (Mannion et al., 2013; Reynolds et al., 2019; Sarah et al., 2019; Mazurek et al., 2019).

Sleep disorders are usually estimated by subjective or objective measurements. Subjective approaches, including sleep questionnaires and sleep diaries (Malow et al., 2006), generally result in reporter bias, which has generally affected the efficacy of subjective assessments and lead to the problems in the diagnosis in sleep disorders (Katz et al., 2018). Thus, objective approaches were reported to be more suitable for the accurate diagnosis of sleep disorders in ASDs (Moore et al., 2017), as they not only quantify various sleep parameters contributing to sleep architecture but also identify unobserved behaviors that could be ignored by parents (Johnson et al., 2016).

There are three main common objective measurements: actigraphy, polysomnography (PSG) and videosomnography. PSG is the gold standard to evaluate sleep disturbance despite its high cost (Schwichtenberg et al., 2018; Enise et al., 2019). The recordings from PSG included continuous sleep parameters and structural sleep parameters. Continuous sleep parameters are sleep efficiency (SE%), sleep latency (SL) or sleep onset latency (SOL), total sleep time (TST), number of wakings (NAs) and wake after sleep onset (WASO); the structural sleep parameters are Stage 1 sleep (S1%), Stage 2 sleep (S2%), slow wave sleep (SWS), rapid eye movement sleep (REM%) and rapid eye movement latency (REML) (Baglioni et al., 2016).

Studies have shown that sleep disorders have an approximate of 25% incidence in typically developed individuals (TDs) and an incidence of over 80% in individuals with ASDs (Le et al., 2001; Owens, 2007). Furthermore, evidence has indicated that sleep disorders have an impact on cognitive function, emotional function and behavioral function (Adams et al., 2014; Serkan et al., 2019; Catherine, 2019). Although it has been reported that ASDs are associated with increased difficulties in falling asleep, shorter TST, longer SL or SOL, lower SE% and more NAs (Malow and Mcgrew, 2008; Elrod and Hood, 2015; Won et al., 2018), studies focusing on ASDs are limited (Richdale and Schreck, 2009).

This study was aim to determine the differences in sleep structure between individuals with ASDs and TDs and explore the potential correlations between these sleep parameters. This will guide further relevant research.

Section snippets

Procedures

We reported this work in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Inclusion and exclusion criteria

Studies that met in the following criteria were included: (a) observational studies (case-control studies or cohort studies); (b) cases involving ASDs with clear diagnostic criteria; (c) controls who were TDs that were reasonably well matched; (d) sleep parameters were recorded by PSG in cases and controls; (e) the extracted data included the means and standard deviations or

Study selection

A total of 872 candidate studies were identified in the selected databases, including 153 duplicate studies. The full text of seventy-nine studies was reviewed after excluding 640 studies by screening the title and abstract. In addition, potentially relevant studies in the reference list were also retrieved, but none of them were eligible for inclusion. Finally, 14 studies (Diomedi et al., 1999; Godbout et al., 2000; Pekka et al., 2004; Malow et al., 2006; Oliviero et al., 2007; Goldman et al.,

Conclusion

Our findings suggested that individuals with ASDs had a shorter TST and a lower SE% which were consistent with the results of a previous study (Elrod and Hood, 2015). The difference was that we utilized all sleep parameter data recorded during PSG, while Elrod and Hood used partial sleep parameter data recorded during PSG as well as actigraphy. In addition, this study showed individuals with ASDs had an increased S1% as a consequence, which had not been reported in the previous literature.

The

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

Xin Chen proposed the concept, designed the study, analyzed the data, and wrote this paper. Xin chen and Haixia Liu were responsible for the primary literature searches, quality assessment and data extraction. They have made equal contributions to this article. Yile Wu performed data inspection and interpretation. All authors modified and edited the language of the article.

Declaration of Competing Interest

None.

Acknowledgement

We will thank the anonymous referees for their comments and suggestions.

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